The devastation led by the COVID-19 pandemic was accompanied by a plethora of misinformation, laden with pseudoscience, hoaxes, and myths, often intertwined with hate speech. This phenomenon was particularly pronounced in India, where the intricate political and communal landscape provided fertile ground. The misinformation, with its elements of hate speech, posed a significant threat to societal cohesion. In response, this article delves into the dynamics of misinformation during the COVID-19 crisis in India, with a specific focus on differentiating general misinformation (GM) from hateful misinformation (HM). To this end, we construct an Indian COVID-19 misinformation dataset collected from various online social and mainstream media and analyze it from various perspectives. Mainly, we focus on temporal evolution, content and topics involved, and emotions and sentiment sensationalism of COVID-19 misinformation. We found the emotions of sadness and fear as key amplifiers of misinformation in general, with negative sentiments dominating HM. Through our comprehensive analysis, we found many such interesting insights and patterns. We also perform hate detection within misinformation content using various unsupervised and supervised learning techniques. Our results show that while GM is relatively easier to identify, it is challenging to detect HM. Overall, deep learning models are found to be more effective than unsupervised methods. By discovering key insights and patterns, this study serves as a foundation for developing robust strategies to combat information disorder.

The problem of infection source detection deals with localizing the infection source in a given network. While the problem has been extensively studied in the past, researchers have mainly focused on simulated infection networks which may not be the correct reflection of the dynamics of real-world infections. More significantly, the existing methods assume that a rumor source lies at the center of an infection network (source-centrality), which is not always true in sparse real-world rumor networks. Due to the randomness of infection flow in such networks, the source may lie away from the center (source-skewness). There is also a lack of real-world infection network datasets to provide a true real-world perspective. Therefore, we revisit the source detection problem and contemplate a shift from mainstream simulations to a real-world paradigm. To this end, we generate two novel rumor network datasets, Cov19-RN and Use20-RN, based on COVID-19 and US Elections 2020 misinformation trends on Twitter (currently$\mathbb {X}$). Besides, inspired by the technicalities inherent to real-world rumor networks, we propose a real-world oriented algorithm called Generalized Exoneration and Prominence based Age, GEPA, for rumor source detection. GEPA addresses the problem of source-skewness to detect rumor sources using the concept of generalized local prominence, which we introduce in this study. Our experiments show that GEPA significantly outperforms the state-of-the-art methods, producing detection rates of 73.6% against 61.5% of the closest competing method on Cov19-RN, and 61.5% against 52.6% of the closest competing method on Use20-RN. To the best of our knowledge, this study is the first such work to deal with source detection in real-world rumor networks and address the problem of source-skewness. Our complete source code, benchmark datasets and detailed results are available athttps://github.com/tesla121/GEPA.

We often see rumors rapidly spreading in online social networks. These are harmful for our society in many ways. Infection source detection is the task of identifying the sources of rumors or any other such infections in social networks, so that appropriate intervention could be performed to control the harm. Researchers have studied this problem under various scenarios, where multisource detection has been of special importance. In this article, we propose a novel infection rate controlled label propagation method for multisource detection calledFROST. It leverages the connection strengths between a pair of nodes in the form of infection rate to capture the implicit information latent within an infection. Initially, labels are assigned to nodes indicating whether the nodes are infected or not. Afterward, the labels are propagated across the network in a controlled manner based on the infection rate. Once the propagation converges, the locally prominent nodes are considered as sources. We compareFROSTagainst six state-of-the-art methods and two heuristic baselines in terms of ten evaluation measures over four social networks datasets. Our results show thatFROSTgenerally outperforms the competing methods across various evaluation measures and datasets. It also estimates the number of sources closer to the actual than the competing methods.FROSTscales effectively for large infections, including when there are infection overlaps, where the competing methods generally lag.

Infection source identification is a well-established problem, having gained a substantial scale of research attention over the years. In this paper, we study the problem by exploiting the idea of the source being the oldest node. For the same, we propose a novel algorithm called Exoneration and Prominence based Age (EPA), which calculates the age of an infected node by considering its prominence in terms of its both infected and non-infected neighbors. These non-infected neighbors hold the key in exonerating an infected node from being the infection source. We also propose a computationally inexpensive variant of EPA, called EPA-LW. Extensive experiments are performed on seven datasets, including 5 real-world and 2 synthetic, of different topologies and varying sizes to demonstrate the effectiveness of the proposed algorithms. We consistently outperform the state-of-the-art single source identification methods in terms of average error distance. To the best of our knowledge, this is the largest scale performance evaluation of the considered problem till date. We also extend EPA to identify multiple sources by developing two new algorithms - one based on K-Means, called EPA_K-Means, and another based on successive identification of sources, called EPA_SSI. Our results show that both EPA_K-Means and EPA_SSI outperform the other multi-source heuristic approaches.

This paper describes the distributed system, network and software architecture, the application development environment, the performance, and the early lessons learned on the ATM LAN testbed Mercuri established at the Honeywell Technology Center, to develop distributed multimedia technologies for real-time control applications. We have developed a client-server-based software architecture on Sun Sparcstation-2s connected by a Fore Systems' ASX-100 ATM switch, with video processing handled by Parallax's X Video cards. The architecture enables network-transparent applications and provides simple primitives for multimedia capture, display, transmission, storage, and retrieval. A real-time multimedia-in-the-loop control application was developed as the vehicle for testing the capabilities and performance of the network. Our test measurements focus on the end-user-level performance metrics such as message throughput and round-trip delay as well as video-frame jitter under no-load and load conditions. Our results show that the maximum burst throughput that can be supported at the user level is 48 Mb/s using AAL 5, while round-trip delays for 4-kbyte messages are about 3 ms. Our experience reveals a number of performance bottlenecks and open issues in using commercial ATM switches for practical applications. Our conclusions are outlined.>